{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x763ab00>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x75f5048>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.ylabel('some numbers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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42d1nBtOWBGP5QF7YC3yISNEtWJPN7aPT+c/SzZx6eCP+1L87zetVT3YsKWHCFIz7DvK5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7692048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=np.arange(-5, 5, 0.1)\n",
    "Y=np.arange(-5, 5, 0.1)\n",
    "x, y=np.meshgrid(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f=17*x**2-16*np.abs(x)*y+17*y**2-225"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cs = plt.contour(x, y, f, 0, colors = 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Zs2Axo2BiRNdVyIikeybb4Dq1YEOHVxXUqhgBbsxQO/ewLRpB7qyiYgUR14eg71HDyAq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5d6b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.   0.   5. ...,   0.   0.   0.]\n",
      " [  0.   0.   0. ...,  10.   0.   0.]\n",
      " [  0.   0.   0. ...,  16.   9.   0.]\n",
      " ..., \n",
      " [  0.   0.   1. ...,   6.   0.   0.]\n",
      " [  0.   0.   2. ...,  12.   0.   0.]\n",
      " [  0.   0.  10. ...,  12.   1.   0.]]\n"
     ]
    }
   ],
   "source": [
    "print(digits.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import jieba.posseg as pseg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import feature_extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----这里输出第 0 类文本的词语tf-idf权重------\n",
      "中国 0.0\n",
      "北京 0.52640543361\n",
      "大厦 0.0\n",
      "天安门 0.0\n",
      "小明 0.0\n",
      "来到 0.52640543361\n",
      "杭研 0.0\n",
      "毕业 0.0\n",
      "清华大学 0.66767854461\n",
      "硕士 0.0\n",
      "科学院 0.0\n",
      "网易 0.0\n",
      "-----这里输出第 1 类文本的词语tf-idf权重------\n",
      "中国 0.0\n",
      "北京 0.0\n",
      "大厦 0.525472749264\n",
      "天安门 0.0\n",
      "小明 0.0\n",
      "来到 0.414288751166\n",
      "杭研 0.525472749264\n",
      "毕业 0.0\n",
      "清华大学 0.0\n",
      "硕士 0.0\n",
      "科学院 0.0\n",
      "网易 0.525472749264\n",
      "-----这里输出第 2 类文本的词语tf-idf权重------\n",
      "中国 0.4472135955\n",
      "北京 0.0\n",
      "大厦 0.0\n",
      "天安门 0.0\n",
      "小明 0.4472135955\n",
      "来到 0.0\n",
      "杭研 0.0\n",
      "毕业 0.4472135955\n",
      "清华大学 0.0\n",
      "硕士 0.4472135955\n",
      "科学院 0.4472135955\n",
      "网易 0.0\n",
      "-----这里输出第 3 类文本的词语tf-idf权重------\n",
      "中国 0.0\n",
      "北京 0.61913029649\n",
      "大厦 0.0\n",
      "天安门 0.78528827571\n",
      "小明 0.0\n",
      "来到 0.0\n",
      "杭研 0.0\n",
      "毕业 0.0\n",
      "清华大学 0.0\n",
      "硕士 0.0\n",
      "科学院 0.0\n",
      "网易 0.0\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    corpus=[\"我 来到 北京 清华大学\",\"他 来到 了 网易 杭研 大厦\", \"小明 硕士 毕业 与 中国 科学院\",\"我 爱 北京 天安门\"]\n",
    "    vectorizer = CountVectorizer()\n",
    "    transformer=TfidfTransformer()\n",
    "    tfidf=transformer.fit_transform(vectorizer.fit_transform(corpus))\n",
    "    word=vectorizer.get_feature_names()\n",
    "    weight=tfidf.toarray()\n",
    "    for i in range(len(weight)):\n",
    "        print(u\"-----这里输出第\", i, u\"类文本的词语tf-idf权重------\")\n",
    "        for j in range(len(word)):\n",
    "            print(word[j], weight[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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